癌症患者通过ePRO平台遵守营养管理:一项机器学习模型研究
Si-Wei Xie1, Jia-Xin Huang2, Hui-Min Qu3
1Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
EClinicalMedicine
|July 21, 2025
概括
超过三分之一的癌症患者在使用电子患者报告结果 (ePRO) 系统时难以达到营养目标. 晚期癌症阶段,表现不佳状态,以及恶心等症状预示着对ePRO指导营养的遵守程度较低.
科学领域:
- 在瘤学瘤学.
- 营养科学 营养科学
- 数字健康数字健康
背景情况:
- 电子患者报告结果 (ePRO) 系统为癌症患者提供个性化的营养管理.
- 坚持ePRO指导的营养干预措施是可变的,并不明白.
- 确定坚持的预测因素对于优化营养策略至关重要.
研究的目的:
- 使用ePRO平台评估遵守总能量摄入量 (TEI) 和总蛋白质摄入量 (TPI) 的目标.
- 确定影响癌症患者遵守ePRO指导的营养管理的关键预测因素.
主要方法:
- 一个多中心,前性的纵向队列研究,8268名癌症患者.
- 坚持定义为实际摄入量与规定的摄入量 (TEI和TPI) 的比例.
- 可解释的机器学习 (LightGBM与SHAP) 和后勤回归用于识别预测因素.
主要成果:
- 33.0%和40.3%的患者未能达到TEI和TPI目标.
- 低坚持的预测因素包括先进的TNM阶段,不良的ECOG状态,更高的PG-SGA得分,血小板升高,步行/睡眠减少和恶心.
- 较高的坚持与女性性别和较高的血清白蛋白,ALT和葡萄糖水平有关.
结论:
- 很大一部分癌症患者无法通过ePRO系统达到营养目标.
- 识别的预测因素可以帮助分层患者,这些患者有着不良坚持的风险.
- 这项研究为改善瘤学中ePRO引导的营养管理策略提供了信息.
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